Hydrogen production system control method and system based on real-time demand, terminal and medium

Abstract

The invention discloses a hydrogen production system control method and system based on real-time requirements, a terminal and a medium in the technical field of hydrogen production system control, and aims to solve the problems that in the prior art, control parameters are difficult to adjust according to the real-time operation condition of a hydrogen production system, so that the hydrogen production efficiency is low, and the work is not stable enough. The method comprises the steps of obtaining state parameters of a hydrogen production process; calculating the load change rate of the electrolytic cell according to the state parameters in the hydrogen production process; the load change rate of the electrolytic cell is compared with a preset threshold value, if the load change rate of the electrolytic cell is not smaller than the preset threshold value, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated through feedforward control, and if the load change rate of the electrolytic cell is smaller than the preset threshold value, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated through fuzzy PID control; the control mode is automatically selected according to the load change rate of the electrolytic cell, and the control parameters can be dynamically adjusted according to the real-time operation condition of the hydrogen production system, so that the hydrogen production process is more stable and efficient.

Description

Technical Field

[0001] This invention relates to a hydrogen production system control method, system, terminal, and medium based on real-time demand, and belongs to the field of hydrogen production system control technology. Background Technology

[0002] Current hydrogen production system control technology has many shortcomings. The deficiencies of existing hydrogen production system control technology mainly lie in four aspects, as follows:

[0003] Parameter staticity: Temperature and current rely on fixed values ​​or manual adjustment, making it impossible to dynamically respond to fluctuations in operating conditions. For example, when renewable energy inputs are unstable, such as varying light intensity and wind speed, hydrogen production power sources cannot automatically adjust parameters to adapt to changes, resulting in low hydrogen production efficiency.

[0004] Lagging energy efficiency monitoring: Traditional power consumption assessment relies on offline detection and lacks real-time feedback capabilities down to the minute level. This makes it difficult for operators to understand the energy consumption during hydrogen production in a timely manner, preventing them from taking timely measures to optimize energy use and resulting in energy waste.

[0005] Strong coupling of multiple parameters: Temperature and current interact (temperature rise → ionic conductivity rise → current efficiency rise; current rise → ohmic heat rise → temperature rise), making manual tuning inefficient; in actual operation, due to the interrelationship between multiple parameters, adjusting one parameter may have a chain reaction on other parameters, increasing the difficulty of control.

[0006] Lack of safety boundaries: Parameter adjustments do not dynamically constrain equipment safety thresholds. When abnormal situations occur during hydrogen production, such as excessively high pressure or temperature, existing control technologies may not be able to react in time, thus affecting the safe operation of the equipment.

[0007] In summary, existing control technologies are insufficient to adjust control parameters based on the real-time operation of the hydrogen production system, resulting in problems such as low hydrogen production efficiency and unstable operation. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a hydrogen production system control method, system, terminal, and medium based on real-time demand. The system automatically selects the control mode according to the rate of change of the electrolyzer load. When the load changes drastically, feedforward control is prioritized to ensure rapid response; when the load is stable, it switches to fuzzy PID control for precise adjustment. The two control modes achieve seamless transition through a smooth switching algorithm, avoiding system oscillation. The system can dynamically adjust control parameters according to the real-time operation of the hydrogen production system, improving the control accuracy of the hydrogen production system and making the hydrogen production process more stable and efficient.

[0009] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution:

[0010] In a first aspect, the present invention provides a hydrogen production system control method based on real-time demand, comprising:

[0011] Obtain the state parameters of the hydrogen production process;

[0012] Calculate the rate of change of electrolyzer load based on the state parameters of the hydrogen production process;

[0013] The rate of change of the electrolytic cell load is compared with a preset threshold. If the rate of change of the electrolytic cell load is not less than the preset threshold, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using feedforward control. If the rate of change of the electrolytic cell load is less than the preset threshold, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using fuzzy PID control.

[0014] The hydrogen production system is controlled based on the first or second opening of the hydrogen-oxygen side pneumatic diaphragm valve.

[0015] Furthermore, the state parameters of the hydrogen production process include the output current of the electrolyzer at various times;

[0016] The specific expression for calculating the rate of change of electrolytic cell load is as follows:

[0017] ;

[0018] In the formula: The rate of change of the electrolytic cell load. For the electrolytic cell at time The output current, For the electrolytic cell at time The output current.

[0019] Furthermore, the calculation of the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve using feedforward control includes:

[0020] Determine the sampling period and obtain the average output current of the electrolytic cell within the sampling period;

[0021] The time delay compensation factor is calculated based on the sampling period, and the specific expression is as follows:

[0022] ;

[0023] In the formula: As the time delay compensation factor, It is a constant. The system lag time is T, and the sampling period is T.

[0024] Based on the average output current of the electrolyzer during the sampling period and the time delay compensation factor, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated, and the specific expression is as follows:

[0025] ;

[0026] In the formula: This represents the first opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This represents the average output current of the electrolytic cell during the sampling period. This is the maximum allowable operating current of the electrolytic cell.

[0027] Furthermore, the state parameters of the hydrogen production process include the pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer;

[0028] The calculation of the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve using fuzzy PID control includes:

[0029] The pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer are input into a pre-built fuzzy controller to obtain the correction amount;

[0030] Based on the correction amount, the PID control quantity is calculated, and the specific expression is as follows:

[0031] ;

[0032] In the formula: Let k be the PID control input for the kth control iteration. , , All are PID parameters. This is the current error. Let j be the error of the (k-1)th control, and j be the sampling time number. This represents the error value at sampling time j. This is a correction amount;

[0033] Based on the PID control input, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated, and the specific expression is as follows:

[0034] ;

[0035] In the formula: This is the second opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This controls the upper limit of the output value.

[0036] Furthermore, it also includes online adaptive adjustment based on the controlled hydrogen production system;

[0037] Specifically, the online adaptive adjustment includes:

[0038] Based on the controlled hydrogen production system, the optimal energy consumption deviation combination is sought within the preset temperature and current ranges through grid partitioning. The optimization model is as follows:

[0039] ;

[0040] In the formula: J is the objective function, representing the performance metric that needs to be minimized; Actual energy consumption For target energy consumption, This is the current highest temperature. For safe temperature threshold, , All are target weight coefficients, and satisfy the following conditions: ;

[0041] The optimal energy consumption deviation combination is obtained by iteratively solving the objective function based on the optimization model to find the minimum value of the objective function.

[0042] Based on the controlled hydrogen production system, adjust the alkaline solution flow rate and separator level, as shown in the following expressions:

[0043] ;

[0044] ;

[0045] In the formula: Q is the target alkali solution flow rate, and H is the target separator liquid level. This is the baseline quantity for the alkaline solution flow rate. This is the reference volume for the separator liquid level. and These are the alkali flow rate adjustment amount and the separator liquid level adjustment amount, respectively, obtained through fuzzy reasoning or table lookup methods based on expert experience.

[0046] Furthermore, this also includes optimizing the hydrogen addition sequence based on the controlled hydrogen production system, specifically including:

[0047] Based on the controlled hydrogen production system and a pre-built hydrogen addition decision model, the hydrogen addition sequence of each hydrogen source is autonomously optimized.

[0048] The specific expression of the hydrogenation decision model is as follows:

[0049] ;

[0050] in, To be the optimal value, For hydrogen source pressure, The maximum operating pressure of the hydrogen source. The current pressure of the hydrogen storage container. This is the critical pressure of the hydrogen storage container. , These are the weighting coefficients;

[0051] The optimal values ​​are sorted according to preset rules, and the hydrogenation source is automatically selected to optimize the hydrogenation sequence.

[0052] Secondly, the present invention provides a hydrogen production system control system based on real-time demand, for implementing the hydrogen production system control method based on real-time demand described in the first aspect, comprising:

[0053] The data acquisition module includes an electricity meter, a hydrogen flow meter, a temperature sensor, and a pressure sensor;

[0054] The control decision module includes a feedforward controller and a fuzzy PID controller;

[0055] The execution module includes a pneumatic diaphragm valve, an alkali pump, and a level regulator.

[0056] Furthermore, it also includes a real-time power consumption closed-loop feedback system, which is used to perform real-time analysis and processing of the data collected by the data acquisition module.

[0057] Thirdly, the present invention provides a terminal, including a processor and a storage medium;

[0058] The storage medium is used to store instructions;

[0059] The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

[0060] Fourthly, a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0062] 1. This hydrogen production system control method based on real-time demand automatically selects the control mode according to the rate of change of the electrolyzer load. When the load changes drastically, feedforward control is used first to ensure rapid response; when the load is stable, it switches to fuzzy PID control to achieve precise adjustment. The two control modes achieve seamless transition through a smooth switching algorithm to avoid system oscillation. It can dynamically adjust the control parameters according to the real-time operation of the hydrogen production system, improve the control accuracy of the hydrogen production system, and make the hydrogen production process more stable and efficient.

[0063] 2. This hydrogen production system control method based on real-time demand decouples the strong coupling relationship between temperature and current, optimizes the alkaline solution flow rate and separator liquid level, and can reduce energy consumption and improve energy utilization efficiency while ensuring hydrogen production.

[0064] 3. This hydrogen production system control method based on real-time demand establishes a hydrogen addition decision model for optimizing the hydrogen addition sequence, enabling precise on-demand decision-making for the hydrogen addition sequence under multiple hydrogen sources. It can autonomously optimize the hydrogen addition sequence based on real-time pressure data of the hydrogen source and real-time internal pressure data of the hydrogen storage container, thereby improving hydrogen utilization and reducing redundancy and waste of low-pressure hydrogen. Attached Figure Description

[0065] Figure 1 This is a schematic flowchart of a hydrogen production system control method based on real-time demand according to an embodiment of the present invention;

[0066] Figure 2 This is a system schematic diagram of a hydrogen production system control system based on real-time demand, provided according to an embodiment of the present invention. Detailed Implementation

[0067] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0068] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0069] Example 1:

[0070] like Figure 1 As shown, the present invention provides a hydrogen production system control method based on real-time demand, comprising:

[0071] Obtain the state parameters of the hydrogen production process;

[0072] Calculate the rate of change of electrolyzer load based on the state parameters of the hydrogen production process;

[0073] The rate of change of the electrolytic cell load is compared with a preset threshold. If the rate of change of the electrolytic cell load is not less than the preset threshold, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using feedforward control. If the rate of change of the electrolytic cell load is less than the preset threshold, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using fuzzy PID control.

[0074] The hydrogen production system is controlled based on the first or second opening of the hydrogen-oxygen side pneumatic diaphragm valve.

[0075] Specifically, the state parameters include data such as the output current, voltage, energy consumption, and hydrogen production of the hydrogen power source, as well as data such as the pressure difference inside and outside the hydrogen-oxygen separator, the temperature of the electrolyzer, the alkaline flow rate, and the separator level. These state parameters can provide real-time feedback on the hydrogen production system's status, offering input for control decisions. In adaptive control, these parameters serve as inputs for fuzzy PID control or feedforward control, dynamically adjusting the opening of the pneumatic diaphragm valve on the hydrogen-oxygen side. Simultaneously, the state parameters can be used to calculate parameters such as actual energy consumption and safe temperature thresholds. Optionally, preset thresholds are set based on historical data and safety standards; for example, the electrolyzer load change rate threshold can be set according to the maximum allowable fluctuation range. Preset thresholds directly affect the selection of the control mode (feedforward control or fuzzy PID control), thereby affecting energy efficiency and safety.

[0076] This invention automatically selects the control mode based on the rate of change of the electrolyzer load. When the load changes drastically, feedforward control is used first to ensure a rapid response; when the load is stable, it switches to fuzzy PID control to achieve precise adjustment. The two control modes achieve a seamless transition through a smooth switching algorithm to avoid system oscillation. It can dynamically adjust the control parameters according to the real-time operation of the hydrogen production system, improve the control accuracy of the hydrogen production system, and make the hydrogen production process more stable and efficient.

[0077] In this embodiment, the state parameters of the hydrogen production process include the output current of the electrolyzer at various times;

[0078] Specifically, the current is collected at various times by a current sensor, and strict synchronization is ensured through time synchronization and filtering. If the deviation between adjacent sampling values ​​exceeds the safety threshold, it is considered a current sensor failure, and redundant sensor data is activated or an alarm is triggered.

[0079] The specific expression for calculating the rate of change of electrolytic cell load is as follows:

[0080] ;

[0081] In the formula: The rate of change of the electrolytic cell load. For the electrolytic cell at time The output current, For the electrolytic cell at time The output current; optionally, the sampling interval at the above time points needs to balance real-time performance and stability: too short an interval is easily affected by noise, while too long an interval will lead to response lag.

[0082] In this embodiment, the calculation of the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve using feedforward control includes:

[0083] A time delay compensation factor is introduced to calculate the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve, and feedforward control is performed on the hydrogen-oxygen side pneumatic diaphragm valve; this control method can respond to changes in the system in advance and reduce the system's lag.

[0084] Determine the sampling period and obtain the average output current of the electrolytic cell within the sampling period;

[0085] The time delay compensation factor is calculated based on the sampling period, and the specific expression is as follows:

[0086] ;

[0087] In the formula: As the time delay compensation factor, It is a constant. The system lag time is T, and the sampling period is T.

[0088] During feedforward control, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve Related to the average output current of the electrolytic cell during the sampling period and the time delay compensation factor, it can be expressed as:

[0089] ;

[0090] in, This represents the core function in the feedforward controller used to calculate the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve;

[0091] In this embodiment, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated based on the average output current of the electrolyzer during the sampling period and the time delay compensation factor. The specific expression is as follows:

[0092] ;

[0093] In the formula: This represents the first opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This represents the average output current of the electrolytic cell during the sampling period. This is the maximum allowable operating current of the electrolytic cell.

[0094] In this embodiment, the state parameters of the hydrogen production process include the pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer;

[0095] The second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using fuzzy PID control. Fuzzy PID control combines the advantages of fuzzy control and PID control, and can better adapt to the nonlinearity and uncertainty of the system, including:

[0096] The pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer are input into a pre-built fuzzy controller to obtain the correction amount;

[0097] The fuzzy controller achieves adaptive control through multivariate input and rule-based reasoning. The system collects the pressure difference (ΔP) of the hydrogen-oxygen separator and the load ratio (η) of the electrolyzer in real time as input variables, and converts the precise quantities into fuzzy linguistic variables through fuzzification. A fuzzy rule base is established, and the control correction quantity Δu is finally obtained by defuzzification using the centroid method. This design enables the system to dynamically adjust the control strategy according to changes in operating conditions.

[0098] Specifically, the matrix representation of the fuzzy rule base is a fuzzy rule table, and the design and construction of the fuzzy rule table are as follows:

[0099] The fuzzy rule table defines the nonlinear relationship between the input variable and the output correction Δu, and its design is based on system characteristics and expert experience.

[0100] Input variable selection: Real-time acquisition of hydrogen-oxygen separator pressure difference and electrolyzer load ratio as inputs; hydrogen-oxygen separator pressure difference reflects gas-liquid balance state, electrolyzer load ratio characterizes load level. These variables are acquired by sensors and normalized to a standard universe of discourse; such as [-1, 1].

[0101] Fuzzy set partitioning: Each input variable is divided into 7 fuzzy subsets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB); the membership function adopts a triangle or trapezoid to cover the entire input range;

[0102] Rule table structure: The rule table is a two-dimensional matrix (7×7) with a total of 49 rules; each rule is in the form of "IF ΔP isA AND η isB, THEN Δu isC", where A, B, and C are fuzzy subsets; the rules are formulated based on the physical characteristics of the hydrogen production process. For example, if ΔP is positive and large (PB) and η is negative and small (NS), then Δu is positive and medium (PM) to quickly compensate for pressure fluctuations.

[0103] Knowledge source: The initial values ​​of the rules are set by domain experts and can be optimized later using historical data (such as genetic algorithm parameter tuning).

[0104] Fuzzy inference mechanism to calculate Δu: Fuzzy inference converts precise input into fuzzy quantities, applies a fuzzy rule table, and outputs a correction quantity Δu. Specific steps include:

[0105] Fuzzification: The precise input values ​​(ΔP and η) are converted into the membership degrees of fuzzy sets through membership functions; for example, if ΔP = 0.5 kPa, its membership degree to PS is 0.7 and its membership degree to PM is 0.3; this process needs to be completed in real time, with a sampling period of 1 second, and is implemented by a microcontroller.

[0106] Rule matching and activation: Traverse the rule table and match the rules activated by the current input; for example, if ΔP belongs to PS and η belongs to NM, then the corresponding rule is triggered; the trigger strength of each rule is determined by the minimum value of the input membership degree (Min operation).

[0107] Output aggregation: The output fuzzy sets (fuzzy values ​​of Δu) of all activation rules are merged into a comprehensive output fuzzy set through a Max operation; the inference mechanism adopts the Mamdani model to ensure the naturalness of rule interaction.

[0108] Defuzzification to obtain Δu: The centroid method is used to convert the output fuzzy set into the precise value Δu.

[0109] ;

[0110] in, Let n be the trigger strength of the i-th rule, and n be the total number of rules. The output value corresponding to this rule (e.g., PS corresponds to a value of 0.5); the range of Δu is preset to [-1, 1], which is used to correct the PID control quantity.

[0111] Based on the correction amount, the PID control quantity is calculated, and the specific expression is as follows:

[0112] ;

[0113] In the formula: Let k be the PID control input for the kth control iteration. , , All are PID parameters. This is the current error. Let j be the error of the (k-1)th control, and j be the sampling time number. This represents the error value at sampling time j. This is a correction amount;

[0114] Based on the PID control input, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated, and the specific expression is as follows:

[0115] ;

[0116] In the formula: This is the second opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This controls the upper limit of the output value.

[0117] Specifically, the calculation of the PID control quantity includes: combining the correction quantity Δu output by the fuzzy controller with the traditional PID control quantity to form an enhanced PID control algorithm; and calculating the current control error of the system. By combining the historical error integral term and the error change rate derivative term, and introducing a fuzzy correction term Δu for dynamic compensation, the PID control quantity is finally converted into the second opening degree of the pneumatic diaphragm valve through normalization. This enables precise control of the opening degree;

[0118] The hydrogen-oxygen side pneumatic diaphragm valve serves as the final actuator. It receives the opening signal calculated by fuzzy PID control. The actuator drive system converts the digital control quantity into an analog signal and precisely controls the valve opening through an electro-pneumatic converter. The system monitors the actual valve opening in real time and forms a closed-loop feedback to ensure control accuracy and response speed.

[0119] In this embodiment, online adaptive adjustment is also performed based on the controlled hydrogen production system.

[0120] Specifically, the online adaptive adjustment includes:

[0121] During the operation of the hydrogen production system, grid optimization and fine optimization of temperature and current are performed periodically to determine the lowest optimal energy consumption deviation ΔE combination.

[0122] Based on the controlled hydrogen production system, the optimal energy consumption deviation combination is sought within the preset temperature and current ranges through grid partitioning. The optimization model is as follows:

[0123] ;

[0124] In the formula: J is the objective function, representing the performance metric that needs to be minimized; Actual energy consumption For target energy consumption, This is the current highest temperature. For safe temperature threshold, , All are target weight coefficients, and satisfy the following conditions: ;

[0125] Based on the optimization model, the minimum value of the objective function is iteratively solved in the preset temperature-current grid space to obtain the optimal energy consumption deviation ΔE combination.

[0126] Based on the controlled hydrogen production system, the alkaline solution flow rate and separator level are dynamically adjusted, as shown in the following expression:

[0127] ;

[0128] ;

[0129] In the formula: Q is the target alkali solution flow rate, and H is the target separator liquid level. This is the baseline quantity for the alkaline solution flow rate. This is the reference volume for the separator liquid level. and These are the alkali flow rate adjustment amount and the separator liquid level adjustment amount, respectively, obtained through fuzzy reasoning or table lookup methods based on expert experience; the output flow rate / liquid level adjustment step size is taken with the deviation of electrolyzer temperature, current, and hydrogen production as inputs, and is subject to safety constraints during the adjustment process.

[0130] This invention decouples the strong coupling relationship between temperature and current, optimizes the alkaline solution flow rate and separator liquid level, and can reduce energy consumption and improve energy utilization efficiency while ensuring hydrogen production; actual tests show that the unit power consumption reduction can reach 5.8-8.2%.

[0131] In this embodiment, the hydrogen addition sequence is optimized based on the controlled hydrogen production system, specifically including:

[0132] The system collects real-time pressure data from hydrogen sources and internal pressure data from hydrogen storage containers, and shares this data with the control system. Based on the established hydrogen addition decision model, the control system autonomously optimizes the hydrogen addition sequence of each hydrogen source to achieve precise on-demand decision-making.

[0133] Based on the controlled hydrogen production system and a pre-built hydrogen addition decision model, the hydrogen addition sequence of each hydrogen source is autonomously optimized.

[0134] The specific expression of the hydrogenation decision model is as follows:

[0135] ;

[0136] in, To be the optimal value, For hydrogen source pressure, The maximum operating pressure of the hydrogen source. The current pressure of the hydrogen storage container. This is the critical pressure of the hydrogen storage container. , These are the weighting coefficients;

[0137] The optimal values ​​are sorted based on preset rules, and hydrogen sources are automatically selected to optimize the hydrogen addition sequence. Optionally, the preset rule is to sort the optimal values ​​of each hydrogen source from largest to smallest, thereby optimizing the hydrogen addition sequence of each hydrogen source and achieving precise on-demand decision-making.

[0138] Specifically, this invention collects pressure data from various hydrogen sources, internal pressure data from hydrogen storage containers, and equipment status parameters in real time through a distributed sensor network; it employs data cleaning and Kalman filtering techniques to fuse multi-source heterogeneous data, establishing a unified data quality assessment system to provide reliable input for decision-making; it constructs a hydrogen refueling sequence decision model based on multi-objective optimization theory, with the core algorithm integrating fuzzy logic and heuristic rules; by establishing a priority quantification evaluation system, it comprehensively considers the hydrogen source pressure status, storage tank safety margin, and system energy efficiency indicators to achieve dynamic optimization of the hydrogen refueling sequence; and it enables accurate prediction of system operation trends, providing forward-looking guidance for optimization decisions.

[0139] This invention establishes a hydrogen refueling decision model for optimizing the hydrogen refueling sequence, enabling precise on-demand decision-making for the hydrogen refueling sequence in the case of multiple hydrogen sources. It can autonomously optimize the hydrogen refueling sequence based on real-time pressure data of the hydrogen source and real-time internal pressure data of the hydrogen storage container, thereby improving hydrogen utilization and reducing redundancy and waste of low-pressure hydrogen.

[0140] This invention solves the problems of static parameters, lagging energy efficiency monitoring, lack of safety boundaries, and inaccurate hydrogenation decisions in existing technologies. It can reduce unit power consumption by 5.8-8.2% and increase the utilization rate of low-pressure hydrogen to over 92%, significantly improving the efficiency, stability, and safety of hydrogen production systems. It can be widely applied to hydrogen production scenarios through water electrolysis.

[0141] Example 2: The hydrogen production system of the present invention adopts an existing modular design. The overall structure includes three core parts: a data acquisition module, a control decision module, and an execution module. Dynamic control is achieved through real-time signal interaction. The data acquisition module integrates an energy meter, a hydrogen flow meter, a temperature sensor, and a pressure sensor, and is responsible for monitoring the status parameters of the hydrogen production process. The control decision module includes a feedforward controller and a fuzzy PID controller, which adaptively selects the control mode according to the load change rate. The execution module includes components such as a pneumatic diaphragm valve, an alkali pump, and a liquid level regulator, which directly regulate the system operation. This structure ensures full coverage of operating conditions and solves the response lag problem of traditional systems.

[0142] The pneumatic diaphragm valve, alkali pump, and level regulator are key components of the actuator module and have a close collaborative relationship. The pneumatic diaphragm valve is installed at the hydrogen-oxygen side outlet and controls the gas flow rate by adjusting its opening degree; the alkali pump is responsible for circulating the electrolyte to maintain the efficiency of the electrolyzer; and the level regulator stabilizes the separator level to prevent gas-liquid mixing. These three components are linked by control signals: for example, when the fuzzy PID controller calculates the valve opening command, the pneumatic diaphragm valve's action affects the system pressure, thereby triggering compensation adjustments in the alkali pump and level regulator, forming a closed-loop feedback. This relationship demonstrates the advantage of multi-parameter decoupling in this invention, improving system stability.

[0143] like Figure 2As shown, this invention provides a hydrogen production system control system based on real-time demand, used to implement the hydrogen production system control method based on real-time demand described in Embodiment 1, including:

[0144] The data acquisition module includes an electricity meter, a hydrogen flow meter, a temperature sensor, and a pressure sensor;

[0145] The control decision module includes a feedforward controller and a fuzzy PID controller;

[0146] The execution module includes a pneumatic diaphragm valve, an alkali pump, and a level regulator.

[0147] Deploy electricity meters and hydrogen flow meters with minute-level accuracy, respectively installed at the power inlet and hydrogen outlet of the hydrogen production power source, to collect real-time data on electricity consumption and hydrogen production.

[0148] Install pressure sensors, temperature sensors, liquid level sensors and other equipment to collect real-time status parameters such as the pressure difference inside and outside the hydrogen-oxygen separator, the temperature of the electrolytic cell, the flow rate of the alkali solution, and the liquid level of the separator.

[0149] It also includes a real-time power consumption closed-loop feedback system, which is used to perform real-time analysis and processing of the data collected by the data acquisition module.

[0150] like Figure 2 As shown, this invention uses a hardware platform to collect data, an adaptive controller to analyze the data and determine the control strategy, and an actuator to dynamically adjust the hydrogenation sequence, etc.

[0151] The data acquisition module uses an RS485 bus to upload data on a minute-by-minute basis, collecting real-time operating data of the hydrogen production power supply and status parameters of the hydrogen production process. The control decision module includes a feedforward controller and a fuzzy PID controller, with built-in safety constraints. It adaptively selects the control mode based on the rate of change of the electrolyzer load, performing two-stage coordinated tuning and hydrogen addition sequence optimization. The execution module receives control signals and adjusts the equipment operating status to achieve precise control of the hydrogen production process. The method dynamically responds to changes in operating conditions and decouples the strong coupling relationship of multiple parameters through real-time data acquisition, adaptive mode selection, multi-stage parameter tuning, and hydrogen addition sequence optimization.

[0152] Feedforward controllers are open-loop control strategies based on disturbance prediction, used in hydrogen production systems to quickly compensate for measurable disturbances and reduce system lag. Fuzzy PID controllers combine the adaptability of fuzzy logic with the precision of PID control to handle the complex characteristics of hydrogen production systems, such as nonlinearity and time-varying parameters. The two controllers can be dynamically switched or integrated through decision logic to achieve full operating condition coverage.

[0153] The real-time power consumption closed-loop feedback system is independent of the hydrogen production controller and monitors the pressure and liquid level difference of the oxygen-side gas-liquid separation unit. When the parameters exceed the set threshold, the safety controller releases pressure through the first valve to prevent hydrogen and oxygen from mixing.

[0154] This invention monitors the status parameters of the hydrogen production system in real time and dynamically constrains equipment safety thresholds, enabling timely detection and handling of abnormal situations, preventing safety issues such as hydrogen-oxygen cross-contamination, and enhancing the safety and reliability of the hydrogen production system. The system is equipped with multiple safety protection measures, including opening limit protection, rate of change limitation, and fault detection functions. When an abnormal operating condition is detected, it automatically switches to a safe mode and triggers an alarm. At the same time, a redundant sensor design is adopted to ensure the reliability and safety of the control system.

[0155] Based on the same inventive concept as Embodiment 1, the specific functional implementation of each module is the same as that in Embodiment 1, and will not be repeated here.

[0156] Example 3:

[0157] This invention also provides a terminal, including a processor and a storage medium;

[0158] The storage medium is used to store instructions;

[0159] The processor is configured to operate according to the instructions to execute the steps of the method described in Embodiment 1.

[0160] Example 4:

[0161] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0162] Since the storage medium provided in this embodiment of the invention can execute the method provided in Embodiment 1 of the invention, it has the corresponding functional modules and beneficial effects for executing the method.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a hydrogen production system based on real-time demand, characterized in that, include: Obtain the state parameters of the hydrogen production process; Calculate the rate of change of electrolyzer load based on the state parameters of the hydrogen production process; The rate of change of the electrolytic cell load is compared with a preset threshold. If the rate of change of the electrolytic cell load is not less than the preset threshold, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using feedforward control. If the rate of change of the electrolytic cell load is less than the preset threshold, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated using fuzzy PID control. The hydrogen production system is controlled based on the first or second opening of the hydrogen-oxygen side pneumatic diaphragm valve.

2. The hydrogen production system control method based on real-time demand according to claim 1, characterized in that, The state parameters of the hydrogen production process include the output current of the electrolyzer at various times; The specific expression for calculating the rate of change of electrolytic cell load is as follows: ； In the formula: The rate of change of the electrolytic cell load. For the electrolytic cell at time The output current, For the electrolytic cell at time The output current.

3. The hydrogen production system control method based on real-time demand according to claim 1, characterized in that, The calculation of the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve using feedforward control includes: Determine the sampling period and obtain the average output current of the electrolytic cell within the sampling period; The time delay compensation factor is calculated based on the sampling period, and the specific expression is as follows: ； In the formula: As the time delay compensation factor, It is a constant. The system lag time is T, and the sampling period is T. Based on the average output current of the electrolyzer during the sampling period and the time delay compensation factor, the first opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated, and the specific expression is as follows: ； In the formula: This represents the first opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This represents the average output current of the electrolytic cell during the sampling period. This is the maximum allowable operating current of the electrolytic cell.

4. The hydrogen production system control method based on real-time demand according to claim 1, characterized in that, The state parameters of the hydrogen production process include the pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer. The calculation of the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve using fuzzy PID control includes: The pressure difference between the inside and outside of the hydrogen-oxygen separator and the load ratio of the electrolyzer are input into a pre-built fuzzy controller to obtain the correction amount; Based on the correction amount, the PID control quantity is calculated, and the specific expression is as follows: ； In the formula: Let k be the PID control input for the kth control iteration. , , All are PID parameters. This is the current error. Let j be the error of the (k-1)th control, and j be the sampling time number. This represents the error value at sampling time j. This is a correction amount; Based on the PID control input, the second opening degree of the hydrogen-oxygen side pneumatic diaphragm valve is calculated, and the specific expression is as follows: ； In the formula: This is the second opening degree of the pneumatic diaphragm valve on the hydrogen-oxygen side. This controls the upper limit of the output value.

5. The hydrogen production system control method based on real-time demand according to claim 1, characterized in that, It also includes online adaptive adjustment based on the controlled hydrogen production system; Specifically, the online adaptive adjustment includes: Based on the controlled hydrogen production system, the optimal energy consumption deviation combination is sought within the preset temperature and current ranges through grid partitioning. The optimization model is as follows: ； In the formula: min means minimization; J is the objective function, representing the performance metric that needs to be minimized; Actual energy consumption For target energy consumption, This is the current highest temperature. For safe temperature threshold, , All are target weight coefficients, and satisfy the following conditions: ; The optimal energy consumption deviation combination is obtained by iteratively solving the objective function based on the optimization model to find the minimum value of the objective function. Based on the controlled hydrogen production system, adjust the alkaline solution flow rate and separator level, as shown in the following expressions: ； ； In the formula: Q is the target alkali solution flow rate, and H is the target separator liquid level. This is the baseline quantity for the alkaline solution flow rate. This is the reference volume for the separator liquid level. and These are the alkali flow rate adjustment amount and the separator liquid level adjustment amount, respectively, obtained through fuzzy reasoning or table lookup methods based on expert experience.

6. The hydrogen production system control method based on real-time demand according to claim 1, characterized in that, This also includes optimizing the hydrogen addition sequence based on the controlled hydrogen production system, specifically including: Based on the controlled hydrogen production system and a pre-built hydrogen addition decision model, the hydrogen addition sequence of each hydrogen source is autonomously optimized. The specific expression of the hydrogenation decision model is as follows: ； in, To be the optimal value, For hydrogen source pressure, The maximum operating pressure of the hydrogen source. The current pressure of the hydrogen storage container. This is the critical pressure of the hydrogen storage container. , These are the weighting coefficients; The optimal values ​​are sorted according to preset rules, and the hydrogenation source is automatically selected to optimize the hydrogenation sequence.

7. A hydrogen production system control system based on real-time demand, used to implement the hydrogen production system control method based on real-time demand as described in any one of claims 1 to 6, characterized in that, include: The data acquisition module includes an electricity meter, a hydrogen flow meter, a temperature sensor, and a pressure sensor; The control decision module includes a feedforward controller and a fuzzy PID controller; The execution module includes a pneumatic diaphragm valve, an alkali pump, and a level regulator.

8. The hydrogen production system control system based on real-time demand according to claim 7, characterized in that, It also includes a real-time power consumption closed-loop feedback system, which is used to perform real-time analysis and processing of the data collected by the data acquisition module.

9. A terminal, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 6.

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